IOmicsPASS: network-based integration of multiomics data for predictive subnetwork discovery.

ABSTRACT: Computational tools for multiomics data integration have usually been designed for unsupervised detection of multiomics features explaining large phenotypic variations. To achieve this, some approaches extract latent signals in heterogeneous data sets from a joint statistical error model, while others use biological networks to propagate differential expression signals and find consensus signatures. However, few approaches directly consider molecular interaction as a data feature, the essential linker between different omics data sets. The increasing availability of genome-scale interactome data connecting different molecular levels motivates a new class of methods to extract interactive signals from multiomics data. Here we developed iOmicsPASS, a tool to search for predictive subnetworks

SUBMITTER: Koh HWL 

PROVIDER: S-EPMC6616462 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature